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On downside risk predictability through liquidity and trading activity: a quantile regression approach

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Author Info

  • Lidia Sanchis-Marco

    ()
    (Dpto. Análisis Económico y Finanzas)

  • Antonio Rubia Serrano

    (Universidad de Alicante)

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    Abstract

    Most downside risk models implicitly assume that returns are a sufficient statistic with which to forecast the daily conditional distribution of a portfolio. In this paper, we address this question empirically and analyze if the variables that proxy for market liquidity and trading conditions convey valid information to forecast the quantiles of the conditional distribution of several representative market portfolios. Using quantile regression techniques, we report evidence of predictability that can be exploited to improve Value at Risk forecasts. Including trading- and spread-related variables improves considerably the forecasting performance.

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    File URL: http://www.ivie.es/downloads/docs/wpasad/wpasad-2011-14.pdf
    File Function: Fisrt version / Primera version, 2011
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    Bibliographic Info

    Paper provided by Instituto Valenciano de Investigaciones Económicas, S.A. (Ivie) in its series Working Papers. Serie AD with number 2011-14.

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    Length: 39 pages
    Date of creation: Jun 2011
    Date of revision:
    Publication status: Published by Ivie
    Handle: RePEc:ivi:wpasad:2011-14

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    Related research

    Keywords: Value at Risk; Basel; Liquidity; Trading Activity.;

    This paper has been announced in the following NEP Reports:

    References

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    1. Bollerslev, Tim & Melvin, Michael, 1994. "Bid--ask spreads and volatility in the foreign exchange market : An empirical analysis," Journal of International Economics, Elsevier, vol. 36(3-4), pages 355-372, May.
    2. Clark, Peter K, 1973. "A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices," Econometrica, Econometric Society, vol. 41(1), pages 135-55, January.
    3. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    4. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    5. Tae-Hwy Lee & Yong Bao & Burak Saltoglu, 2006. "Evaluating predictive performance of value-at-risk models in emerging markets: a reality check," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(2), pages 101-128.
    6. Goffe, William L. & Ferrier, Gary D. & Rogers, John, 1994. "Global optimization of statistical functions with simulated annealing," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 65-99.
    7. Keith Kuester & Stefan Mittnik & Marc S. Paolella, 2006. "Value-at-Risk Prediction: A Comparison of Alternative Strategies," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 4(1), pages 53-89.
    8. Engle, Robert F. & Manganelli, Simone, 2001. "Value at risk models in finance," Working Paper Series 0075, European Central Bank.
    9. George Kouretas & Leonidas Zarangas, 2005. "Conditional autoregressive valu at risk by regression quantile: Estimatingmarket risk for major stock markets," Working Papers 0521, University of Crete, Department of Economics.
    10. Stoll, Hans R, 1989. " Inferring the Components of the Bid-Ask Spread: Theory and Empirical Tests," Journal of Finance, American Finance Association, vol. 44(1), pages 115-34, March.
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